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Statistics need not be dull and dry! Engage and inspire your students with Statistics Alive! Presenting essential content on statistical analysis in short, digestible modules, this text is written in a conversational tone with anecdotal stories and light-hearted humor; it’s an enjoyable read that will ensure your students are always prepared for class.

Students are shown the underlying logic to what they're learning, and well-crafted practice and self-check features help ensure that that new knowledge sticks. Coverage of probability theory and mathematical proofs is complemented by expanded conceptual coverage. In the Third Edition, new coauthor Matthew Price includes simplified practice problems and increased coverage of conceptual statistics, integrated discussions of effect size with hypothesis testing, and new coverage of ethical practices for conducting research.

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Statistics Alive!, Third Edition + Student Study Guide
ISBN: 978-1-0718-3088-8


 
List of Figures
 
List of Tables
 
Preface
 
Supplemental Material for Use With Statistics Alive!
 
Acknowledgments
 
About the Authors
 
PART I. PRELIMINARY INFORMATION: “FIRST THINGS FIRST”
 
Module 1. Math Review, Vocabulary, and Symbols
Getting Started

 
Common Terms and Symbols in Statistics

 
Fundamental Rules and Procedures for Statistics

 
More Rules and Procedures

 
 
Module 2. Measurement Scales
What Is Measurement?

 
Scales of Measurement

 
Continuous Versus Discrete Variables

 
Real Limits

 
 
PART II. TABLES AND GRAPHS: “ON DISPLAY”
 
Module 3. Frequency and Percentile Tables
Why Use Tables?

 
Frequency Tables

 
Relative Frequency or Percentage Tables

 
Grouped Frequency Tables

 
Percentile and Percentile Rank Tables

 
SPSS Connection

 
 
Module 4. Graphs and Plots
Why Use Graphs?

 
Graphing Continuous Data

 
Symmetry, Skew, and Kurtosis

 
Graphing Discrete Data

 
SPSS Connection

 
 
PART III. CENTRAL TENDENCY: “BULL’S-EYE”
 
Module 5. Mode, Median, and Mean
What Is Central Tendency?

 
Mode

 
Median

 
Mean

 
Skew and Central Tendency

 
SPSS Connection

 
 
PART IV. DISPERSION: “FROM HERE TO ETERNITY”
 
Module 6. Range, Variance, and Standard Deviation
What Is Dispersion?

 
Range

 
Variance

 
Standard Deviation

 
Mean Absolute Deviation

 
Controversy: N Versus n - 1

 
SPSS Connection

 
 
PART V. THE NORMAL CURVE AND STANDARD SCORES: “WHAT’S THE SCORE?”
 
Module 7. Percent Area and the Normal Curve
What Is a Normal Curve?

 
History of the Normal Curve

 
Uses of the Normal Curve

 
Looking Ahead

 
 
Module 8. z Scores
What Is a Standard Score?

 
Benefits of Standard Scores

 
Calculating z Scores

 
Comparing Scores Across Different Tests

 
SPSS Connection

 
 
Module 9. Score Transformations and Their Effects
Why Transform Scores?

 
Effects on Central Tendency

 
Effects on Dispersion

 
A Graphic Look at Transformations

 
Summary of Transformation Effects

 
Some Common Transformed Scores

 
Looking Ahead

 
 
PART VI. PROBABILITY: “ODDS ARE”
 
Module 10. Probability Definitions and Theorems
Why Study Probability?

 
Probability as a Proportion

 
Equally Likely Model

 
Mutually Exclusive Outcomes

 
Addition Theorem

 
Independent Outcomes

 
Multiplication Theorem

 
A Brief Review

 
Probability and Inference

 
 
Module 11. The Binomial Distribution
What Are Dichotomous Events?

 
Finding Probabilities by Listing and Counting

 
Finding Probabilities by the Binomial Formula

 
Finding Probabilities by the Binomial Table

 
Probability and Experimentation

 
Looking Ahead

 
Nonnormal Data

 
 
PART VII. INFERENTIAL THEORY: “OF TRUTH AND RELATIVITY”
 
Module 12. Sampling, Variables, and Hypotheses
From Description to Inference

 
Sampling

 
Variables

 
Hypotheses

 
 
Module 13. Errors and Significance
Random Sampling Revisited

 
Sampling Error

 
Significant Difference

 
The Decision Table

 
Type I Error

 
Type II Error

 
 
Module 14. The z Score as a Hypothesis Test
Inferential Logic and the z Score

 
Constructing a Hypothesis Test for a z Score

 
Looking Ahead

 
 
PART VIII. THE ONE-SAMPLE TEST: “ARE THEY FROM OUR PART OF TOWN?”
 
Module 15. Standard Error of the Mean
Central Limit Theorem

 
Sampling Distribution of the Mean

 
Calculating the Standard Error of the Mean

 
Sample Size and the Standard Error of the Mean

 
Looking Ahead

 
 
Module 16. Normal Deviate Z Test
Prototype Logic and the Z Test

 
Calculating a Normal Deviate Z Test

 
Examples of Normal Deviate Z Tests

 
Decision Making With a Normal Deviate Z Test

 
Looking Ahead

 
 
Module 17. One-Sample t Test
Z Test Versus t Test

 
Comparison of Z-Test and t-Test Formulas

 
Degrees of Freedom

 
Biased and Unbiased Estimates

 
When Do We Reject the Null Hypothesis?

 
One-Tailed Versus Two-Tailed Tests

 
The t Distribution Versus the Normal Distribution

 
The t Table Versus the Normal Curve Table

 
Calculating a One-Sample t Test

 
Interpreting a One-Sample t Test

 
Looking Ahead

 
SPSS Connection

 
 
Module 18. Interpreting and Reporting One-Sample t: Error, Confidence, and Parameter Estimates
What It Means to Reject the Null

 
Refining Error

 
Decision Making With a One-Sample t Test

 
Dichotomous Decisions Versus Reports of Actual p

 
Parameter Estimation: Point and Interval

 
SPSS Connection

 
 
PART IX. THE TWO-SAMPLE TEST: “OURS IS BETTER THAN YOURS”
 
Module 19. Standard Error of the Difference Between the Means
One-Sample Versus Two-Sample Studies

 
Sampling Distribution of the Difference Between the Means

 
Calculating the Standard Error of the Difference Between the Means

 
Importance of the Size of the Standard Error of the Difference Between the Means

 
Looking Ahead

 
 
Module 20. t Test With Independent Samples and Equal Sample Sizes
A Two-Sample Study

 
Inferential Logic and the Two-Sample t Test

 
Calculating a Two-Sample t Test

 
Interpreting a Two-Sample t Test

 
Looking Ahead

 
SPSS Connection

 
 
Module 21. t Test With Unequal Sample Sizes
What Makes Sample Sizes Unequal?

 
Comparison of Special-Case and Generalized Formulas

 
Calculating a t Test With Unequal Sample Sizes

 
Interpreting a t Test With Unequal Sample Sizes

 
SPSS Connection

 
 
Module 22. t Test With Related Samples
What Makes Samples Related?

 
Comparison of Special-Case and Related-Samples Formulas

 
Advantage and Disadvantage of Related Samples

 
Direct-Difference Formula

 
Calculating a t Test With Related Samples

 
Interpreting a t Test With Related Samples

 
SPSS Connection

 
 
Module 23. Interpreting and Reporting Two-Sample t: Error, Confidence, and Parameter Estimates
What Is Confidence?

 
Refining Error and Confidence

 
Decision Making With a Two-Sample t Test

 
Dichotomous Decisions Versus Reports of Actual p

 
Parameter Estimation: Point and Interval

 
SPSS Connection

 
 
PART X. THE MULTISAMPLE TEST: “OURS IS BETTER THAN YOURS OR THEIRS”
 
Module 24. ANOVA Logic: Sums of Squares, Partitioning, and Mean Squares
When Do We Use ANOVA?

 
ANOVA Assumptions

 
Partitioning of Deviation Scores

 
From Deviation Scores to Variances

 
From Variances to Mean Squares

 
From Mean Squares to F

 
Looking Ahead

 
 
Module 25. One-Way ANOVA: Independent Samples and Equal Sample Sizes
What Is a One-Way ANOVA?

 
Inferential Logic and ANOVA

 
Deviation Score Method

 
Raw Score Method

 
Remaining Steps for Both Methods: Mean Squares and F

 
Interpreting a One-Way ANOVA

 
The ANOVA Summary Table

 
SPSS Connection

 
 
PART XI. POST HOC TESTS: “SO WHO’S RESPONSIBLE?”
 
Module 26. Tukey HSD Test
Why Do We Need a Post Hoc Test?

 
Calculating the Tukey HSD

 
Interpreting the Tukey HSD

 
SPSS Connection

 
 
Module 27. Scheffé Test
Why Do We Need a Post Hoc Test?

 
Calculating the Scheffé

 
Interpreting the Scheffé

 
SPSS Connection

 
 
PART XII. MORE THAN ONE INDEPENDENT VARIABLE: “DOUBLE DUTCH JUMP ROPE”
 
Module 28. Main Effects and Interaction Effects
What Is a Factorial ANOVA?

 
Factorial ANOVA Designs

 
Number and Type of Hypotheses

 
Main Effects

 
Interaction Effects

 
Looking Ahead

 
 
Module 29. Factorial ANOVA
Review of Factorial ANOVA Designs

 
Data Setup and Preliminary Expectations

 
Sums of Squares Formulas

 
Calculating Factorial ANOVA Sums of Squares: Raw Score Method

 
Factorial Mean Squares and Fs

 
Interpreting a Factorial F Test

 
The Factorial ANOVA Summary Table

 
SPSS Connection

 
 
PART XIII. NONPARAMETRIC STATISTICS: “WITHOUT FORM OR VOID”
 
Module 30. One-Variable Chi-Square: Goodness of Fit
What Is a Nonparametric Test?

 
Chi-Square as a Goodness-of-Fit Test

 
Formula for Chi-Square

 
Inferential Logic and Chi-Square

 
Calculating a Chi-Square Goodness of Fit

 
Interpreting a Chi-Square Goodness of Fit

 
Looking Ahead

 
SPSS Connection

 
 
Module 31. Two-Variable Chi-Square: Test of Independence
Chi-Square as a Test of Independence

 
Prerequisites for a Chi-Square Test of Independence

 
Formula for a Chi-Square

 
Finding Expected Frequencies

 
Calculating a Chi-Square Test of Independence

 
Interpreting a Chi-Square Test of Independence

 
SPSS Connection

 
 
PART XIV. EFFECT SIZE AND POWER: “HOW MUCH IS ENOUGH?”
 
Module 32. Measures of Effect Size
What Is Effect Size?

 
For Two-Sample t Tests

 
For ANOVA F Tests

 
For Chi-Square Tests

 
 
Module 33. Power and the Factors Affecting It
What Is Power?

 
Factors Affecting Power

 
Putting It Together: Alpha, Power, Effect Size, and Sample Size

 
Looking Ahead

 
 
PART XV. CORRELATION: “WHITHER THOU GOEST, I WILL GO”
 
Module 34. Relationship Strength and Direction
Experimental Versus Correlational Studies

 
Plotting Correlation Data

 
Relationship Strength

 
Relationship Direction

 
Linear and Nonlinear Relationships

 
Outliers and Their Effects

 
Looking Ahead

 
SPSS Connection

 
 
Module 35. Pearson r
What Is a Correlation Coefficient?

 
Calculation of a Pearson r

 
Formulas for Pearson r

 
z-Score Scatterplots and r

 
Calculating Pearson r: Deviation Score Method

 
Interpreting a Pearson r Coefficient

 
Looking Ahead

 
SPSS Connection

 
 
Module 36. Correlation Pitfalls
Effect of Sample Size on Statistical Significance

 
Statistical Significance Versus Practical Importance

 
Effect of Restriction in Range

 
Effect of Sample Heterogeneity or Homogeneity

 
Effect of Unreliability in the Measurement Instrument

 
Correlation Versus Causation

 
 
PART XVI. LINEAR PREDICTION: “YOU’RE SO PREDICTABLE”
 
Module 37. Linear Prediction
Correlation Permits Prediction

 
Logic of a Prediction Line

 
Equation for the Best-Fitting Line

 
Using a Prediction Equation to Predict Scores on Y

 
Another Calculation Example

 
SPSS Connection

 
 
Module 38. Standard Error of Prediction
What Is a Confidence Interval?

 
Correlation and Prediction Error

 
Distribution of Prediction Error

 
Calculating the Standard Error of Prediction

 
Using the Standard Error of Prediction to Calculate Confidence Intervals

 
Factors Influencing the Standard Error of Prediction

 
Another Calculation Example

 
 
Module 39. Introduction to Multiple Regression
What Is Regression?

 
Prediction Error, Revisited

 
Why Multiple Regression?

 
The Multiple Regression Equation

 
Multiple Regression and Predicted Variance

 
Hypothesis Testing in Multiple Regression

 
An Example

 
The General Linear Model

 
SPSS Connection

 
 
PART XVII. REVIEW: “SAY IT AGAIN, SAM”
 
Module 40. Selecting the Appropriate Analysis
Review of Descriptive Methods

 
Review of Inferential Methods

 
 
Appendix A: Normal Curve Table
 
Appendix B: Binomial Table
 
Appendix C: t Table
 
Appendix D: F Table (ANOVA)
 
Appendix E: Studentized Range Statistic (for Tukey HSD)
 
Appendix F: Chi-Square Table
 
Appendix G: Correlation Table
 
Appendix H: Odd Solutions to Textbook Exercises
 
References
 
Index

Supplements

Instructor Resource Site
edge.sagepub.com/steinberg3e

SAGE Edge for instructors
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  • A password-protected site for complete and protected access to all text-specific instructor resources.
  • A test bank that provides a diverse range of ready-to-use options that save you time. You can also easily edit any question and/or insert your own personalized questions.
  • Editable, chapter-specific PowerPoint® slides that offer complete flexibility for creating a multimedia presentation for your course.
  • Solutions to chapter exercises cover key concepts, and may be used for individual or group settings.
Student study site
SAGE Edge for students enhances learning, it’s easy to use, and offers:
  • An open-access site that makes it easy for students to maximize their study time, anywhere, anytime.
  • eFlashcards that strengthen understanding of key terms and concepts.
  • Chapter summaries with learning objectives that reinforce the most important material.
  • SPSS Data Sets and variable lists.

Key features
NEW TO THIS EDITION:
  • More emphasis on the concepts behind statistics, not just the theory and equations, help students understand the meaning and applications of statistics in the real world.
  • The number of exercises has doubled from the previous edition, giving students additional practice.
  • An introduction to multiple regression and the General Linear Model (GLM) has been streamlined and is presented conceptually only, with examples in SPSS.
  • Examples are not only solved manually within the textbook narrative, but also shown as software output in the new “SPSS Connection” sections. These sections show output as the student would see it and give detailed instructions for obtaining the output, making a separate SPSS instruction manual unnecessary.
  • Answers to odd-numbered exercises are provided at the end of the textbook for the student’s convenience, with answers to the even-numbered exercises reserved for the instructor.
  • More attention is given to the rationale and theory behind hypothesis testing, with a reduction on the focus of computation by hand.
  • The discussion of confidence intervals and interval estimates has been expanded throughout the text.
  • Additional graphing features like whisker and box plots give students more ways to display their data.

KEY FEATURES:

  • The modular format of the text breaks content into digestible components.
  • Each module begins with a set of learning objectives and a list of terms and symbols provide both a scaffold for what to expect of that day’s reading and a reference for finding key information.
  • Check Yourself! boxes throughout the text reinforce learning soon after key concepts have been taught.
  • Practice exercises are dispersed throughout the modules as subtopics covered.
  • Stress-busting cartoons are dispersed throughout while quips in the margin sidebars, presented as associated topics, appeal to the quick-transitions learning style of today’s student.

For instructors

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